CC Fisher — Cyber Cycle Fisher

Applies the Fisher Transform to the normalised Cyber Cycle oscillator, converting cycle measurements into a near-Gaussian probability distribution; signal is the one-bar-lagged fisher value.

Inputs: [real]  |  Options: [alpha]  |  Outputs: [fisher, signal]  |  Optional: [trendmode, cycle, peak]

Minimum bars

This indicator requires approximately 56 bars of input before producing meaningful output. Use at least 80 bars for reliable results. Pass alpha = 0.0 to let the indicator choose the smoothing factor automatically.

Basic

RustPythonNode.jsWASM

use tulip_rs::indicators::ccfisher::indicator;

let close = vec![
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00_f64,
];

// alpha = 0.0 to use automatic smoothing
let (outputs, _state) = indicator(&[close.as_slice()], &[0.0], None).unwrap();
println!("Fisher: {:?}", outputs[0]);
println!("Signal: {:?}", outputs[1]);

// State continuation
let n = close.len() - 5;
let partial = close[..n].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[0.0], None).unwrap();
println!("Partial Fisher: {:?}", outputs2[0]);

let rest = close[n..].to_vec();
let continued = state.batch_indicator(&[rest.as_slice()], None).unwrap();
println!("Continued Fisher:  {:?}", continued[0]);
println!("Continued Signal:  {:?}", continued[1]);

import numpy as np
import tulip_rs

close = np.array([
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
], dtype=np.float64)

# alpha = 0.0 to use automatic smoothing
outputs, state = tulip_rs.indicators.ccfisher.indicator([close], [0.0])
print("Fisher:", outputs[0])
print("Signal:", outputs[1])

# State continuation
partial = close[:-5]
outputs2, state = tulip_rs.indicators.ccfisher.indicator([partial], [0.0])
rest = close[-5:]
continued = state.batch_indicator([rest])
print("Continued Fisher: ", continued[0])
print("Continued Signal: ", continued[1])

import * as ti from 'tulip-rs-node';

const close = Float64Array.from([
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
]);

// alpha = 0.0 to use automatic smoothing
const [outputs, state] = ti.ccfisher.indicator([close], [0]);
console.log('Fisher:', outputs[0]);
console.log('Signal:', outputs[1]);

// State continuation
const [, state2] = ti.ccfisher.indicator([close.slice(0, -5)], [0]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Fisher:', continued[0]);
console.log('Continued Signal:', continued[1]);

import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';

await init(); // bundler resolves the WASM asset automatically

const close = [
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
];

// alpha = 0.0 to use automatic smoothing
const [outputs, state] = ti.ccfisher.indicator([close], [0]);
console.log('Fisher:', outputs[0]);
console.log('Signal:', outputs[1]);

// State continuation
const [, state2] = ti.ccfisher.indicator([close.slice(0, -5)], [0]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Fisher:', continued[0]);
console.log('Continued Signal:', continued[1]);

Optional Outputs

RustPythonNode.jsWASM

ccfisher exposes 3 optional outputs: trendmode, cycle, peak. Pass a boolean mask as the third argument — one bool per optional output, in order.

use tulip_rs::indicators::ccfisher::indicator;

let close = vec![
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00_f64,
];

let mask = [true, true, true]; // one per optional output
let (outputs, _state) = indicator(&[close.as_slice()], &[0.0], Some(&mask)).unwrap();

let fisher    = &outputs[0]; // fisher (primary)
let signal    = &outputs[1]; // signal (primary)
let trendmode = &outputs[2]; // trendmode (optional — requested)
let cycle     = &outputs[3]; // cycle (optional — requested)
let peak      = &outputs[4]; // peak (optional — requested)

import numpy as np
import tulip_rs

close = np.array([
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
], dtype=np.float64)

outputs, state = tulip_rs.indicators.ccfisher.indicator(
    [close], [0.0],
    optional_outputs=[True, True, True],
)

fisher    = outputs[0]  # fisher (primary)
signal    = outputs[1]  # signal (primary)
trendmode = outputs[2]  # trendmode (optional — requested)
cycle     = outputs[3]  # cycle (optional — requested)
peak      = outputs[4]  # peak (optional — requested)

ccfisher exposes 3 optional outputs: trendmode, cycle, peak.

const [allOut] = ti.ccfisher.indicator([close], [0], [true, true, true]);
const fisher    = allOut[0]; // primary
const signal    = allOut[1]; // primary
const trendmode = allOut[2]; // optional 0: trendmode
const cycle     = allOut[3]; // optional 1: cycle
const peak      = allOut[4]; // optional 2: peak

The WASM API is identical to Node.js — pass the boolean mask as the third argument.

const [allOut] = ti.ccfisher.indicator([close], [0], [true, true, true]);
const fisher    = allOut[0]; // primary
const signal    = allOut[1]; // primary
const trendmode = allOut[2]; // optional 0: trendmode
const cycle     = allOut[3]; // optional 1: cycle
const peak      = allOut[4]; // optional 2: peak

SIMD

RustPythonNode.js

By assets — same alpha applied to 4 assets in parallel:

use tulip_rs::indicators::ccfisher::indicator_by_assets;

let a1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let a2 = vec![86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36_f64];
let a3 = vec![78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36_f64];
let a4 = vec![83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05_f64];

let inputs: [&[&[f64]; 1]; 4] = [
    &[a1.as_slice()],
    &[a2.as_slice()],
    &[a3.as_slice()],
    &[a4.as_slice()],
];

let results = indicator_by_assets::<4>(&inputs, &[0.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {} Fisher: {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} Signal: {:?}", i + 1, asset_outputs[1]);
}

By options — same asset, 4 different alpha values in parallel:

use tulip_rs::indicators::ccfisher::indicator_by_options;

let close = vec![81.59, 81.06, 82.87, 83.00, 83.61,
                 83.15, 82.84, 83.99, 84.55, 84.36_f64];

let opts: [&[f64; 1]; 4] = [&[0.0], &[0.1], &[0.2], &[0.3]];

let results = indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
    println!("Alpha set {} Fisher: {:?}", i + 1, opt_outputs[0]);
    println!("Alpha set {} Signal: {:?}", i + 1, opt_outputs[1]);
}

By assets — same alpha applied to N assets in parallel (must be 2, 4, 8, or 16):

import numpy as np
import tulip_rs

close = np.array([
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
], dtype=np.float64)

simd_inputs = [[close], [close + 5.0], [close - 3.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.ccfisher.simd_by_assets(simd_inputs, [0.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1} Fisher: {out[0]}")
    print(f"Asset {i + 1} Signal: {out[1]}")

By options — same asset, N different alpha values in parallel:

import numpy as np
import tulip_rs

close = np.array([
    81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36,
    85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
    88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
    90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
    93.50, 94.10, 94.80, 95.20, 95.70, 96.30, 96.80, 97.10, 97.60, 98.20,
    98.70, 99.10, 99.80, 100.20, 100.70, 101.30, 101.80, 102.10, 102.60, 103.20,
    103.70, 104.10, 104.80, 105.20, 105.70, 106.30, 106.80, 107.10, 107.60, 108.20,
    108.70, 109.10, 109.80, 110.20, 110.70, 111.30, 111.80, 112.10, 112.60, 113.00,
], dtype=np.float64)

simd_options = [[0.0], [0.1], [0.2], [0.3]]
outputs_list, states = tulip_rs.indicators.ccfisher.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Alpha set {i + 1} Fisher: {out[0]}")
    print(f"Alpha set {i + 1} Signal: {out[1]}")

By assets — same alpha applied to 4 assets in parallel:

const simdInputs = [
    [close.slice()],
    [close.map(v => v + 5.0)],
    [close.map(v => v - 3.0)],
    [close.map(v => v * 1.02)],
];
const [results] = ti.ccfisher.simdByAssets(simdInputs, [0]);
results.forEach((out, i) => console.log(`Asset ${i + 1} Fisher:`, out[0], 'Signal:', out[1]));

By options — same asset, 4 different alpha values in parallel:

const simdOptions = [[0], [0.1], [0.2], [0.3]];
const [results] = ti.ccfisher.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Alpha set ${i + 1} Fisher:`, out[0]));